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DPCD: A Quality Assessment Database for Dynamic Point Clouds

Recently, the advancements in Virtual/Augmented Reality (VR/AR) have driven the demand for Dynamic Point Clouds (DPC). Unlike static point clouds, DPCs are capable of capturing temporal changes within objects or scenes, offering a more accurate simulation of the real world. While significant progress has been made in the quality assessment research of static point cloud, little study has been done on Dynamic Point Cloud Quality Assessment (DPCQA), which hinders the development of quality-oriented applications, such as interframe compression and transmission in practical scenarios. Therefore, we introduce a large-scale DPCQA database, named DPCD, which includes 15 reference DPCs and 525 distorted DPCs from seven types of lossy compression and noise distortion. By rendering these samples to Processed Video Sequences (PVS), a comprehensive subjective experiment is conducted to obtain Mean Opinion Scores (MOS) from 21 viewers for analysis.

Dataset Details

Dataset Description

  • Reference: longdress, loot, soldier, redandblack, dancer, model, basketball-player, exercise, AxeGuy, Matis, and Rafa2, mitch, thomas, football, levi.
  • Distortion types: G-PCC-Octree-RAHT, G-PCC-Trisoup-RAHT, V-PCC-C2RA, D-DPCC, CN, DS, GGN.

Dataset Structure

  β”œβ”€β”€ root
  β”‚   β”œβ”€β”€ MOS.csv
  β”‚   β”œβ”€β”€ reference
  β”‚   β”‚   β”œβ”€β”€ AxeGuy
  β”‚   β”‚   β”‚   β”œβ”€β”€ ply
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_000.ply
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_001.ply
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_299.ply
  β”‚   β”‚   β”‚   β”œβ”€β”€ output
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ images
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_000.png
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_001.png
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_299.png
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy.mp4
  β”‚   β”‚   β”œβ”€β”€ basketball-player
  β”‚   β”‚   β”œβ”€β”€ dancer
  β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”œβ”€β”€ thomas
  ...
  β”‚   β”œβ”€β”€ CN
  β”‚   β”‚   β”œβ”€β”€ AxeGuy_CN
  β”‚   β”‚   β”‚   β”œβ”€β”€ 10
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ply
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_000_CN_10.ply
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_001_CN_10.ply
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_299_CN_10.ply
  β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ output
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ images
  β”‚   β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_000_CN_10.png
  β”‚   β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_001_CN_10.png
  β”‚   β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_299_CN_10.png
  β”‚   β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ AxeGuy_CN_10.mp4
  β”‚   β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”‚   β”œβ”€β”€ 70
  β”‚   β”‚   β”œβ”€β”€ basketball-player_CN
  β”‚   β”‚   β”œβ”€β”€ dancer_CN
  β”‚   β”‚   β”œβ”€β”€ ...
  β”‚   β”‚   β”œβ”€β”€ thomas_CN
  ...
  β”‚   β”œβ”€β”€ C2RA
  β”‚   β”‚   β”œβ”€β”€ ...
  ...
  β”‚   β”œβ”€β”€ ddpcc
  β”‚   β”‚   β”œβ”€β”€ ...
  ...
  β”‚   β”œβ”€β”€ DS
  β”‚   β”‚   β”œβ”€β”€ ...
  ...
  β”‚   β”œβ”€β”€ GGN
  β”‚   β”‚   β”œβ”€β”€ ...
  ...
  β”‚   β”œβ”€β”€ octree-raht
  β”‚   β”‚   β”œβ”€β”€ ...
  ...
  β”‚   β”œβ”€β”€ trisoup-raht
  β”‚   β”‚   β”œβ”€β”€ ...
  ...

Dataset Card Authors

Yating Liu, Yujie Zhang, Qi Yang, Yiling Xu, Zhu Li, Ye-Kui Wang

Dataset Card Contact

{Olivialyt, yujie19981026, yl.xu}@sjtu.edu.cn, {qiyang, lizhu}@umkc.edu, {yekui.wang}@bytedance.com

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